The DOI matches its Crossref record; 114 of the 114 references it cites were found in a registry or an index; 24 of the 83 sources its claims cite were checked for support.
Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain
Open accessPublished version of a preprint
Abstract
The mammalian brain is composed of diverse, specialized cell populations. To systematically ascertain and learn from these cellular specializations, we used Drop-seq to profile RNA expression in 690,000 individual cells sampled from 9 regions of the adult mouse brain. We identified 565 transcriptionally distinct groups of cells using computational approaches developed to distinguish biological from technical signals. Cross-region analysis of these 565 cell populations revealed features of brain organization, including a gene-expression module for synthesizing axonal and presynaptic components, patterns in the co-deployment of voltage-gated ion channels, functional distinctions among the cells of the vasculature and specialization of glutamatergic neurons across cortical regions. Systematic neuronal classifications for two complex basal ganglia nuclei and the striatum revealed a rare population of spiny projection neurons. This adult mouse brain cell atlas, accessible through interactive online software (DropViz), serves as a reference for development, disease, and evolution.
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Claims the paper makes
24 of 83 cited sources checked · 10 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“We then grouped cells based on combinations of “biological IC” cell loadings into 565 subclusters using network-based clustering”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Inference of Cell Types and States Using Independent Components Analysis
What the sources say
- Partly supportedcites Shekhar et al., 2016 sourceExperimental — accuracy not yet measured
The Methods section (page unavailable) supports the use of graph-based clustering, but says cells were clustered using scores on 37 significant principal components, not biological independent-component loadings. It reports 30 initial Louvain-Jaccard clusters, while the Results section (page unavailable) reports 26 putative cell-type clusters—not 565 subclusters. (LLM, conf=0.88)
- Partly supportedcites Waltman and van Eck, 2013 sourceExperimental — accuracy not yet measured
The Conclusions (p. 19) say the source introduces an algorithm for modularity-based community detection in large networks, supporting its use as a network-clustering method. The source does not report grouping cells by biological IC loadings or obtaining 565 subclusters; in the citing sentence, “We” refers to the citing authors, not Waltman and van Eck. (LLM, conf=0.88)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Transcriptional clusters derived from single-cell RNA-seq correspond to biologically meaningful cell types or states.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This transcriptional pattern involved a constellation of genes that underlie axonal and pre-synaptic function. We call this signal the “Neurofilament IC,” because three of the most strongly contributing genes encode the neurofilament subunits of the axonal cytoskeleton (Nefl, Nefm, and Nefh)”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > A Neuronal Transcriptional Program Related to Axon Function
What the sources say
- Partly supportedcites Yuan et al., 2012 sourceExperimental — accuracy not yet measured
The passage describing neurofilament composition identifies NEFL, NEFM and NEFH as genes for light, medium and heavy neurofilament subunits, supporting the reason for the label. It does not describe a transcriptional pattern, an independent component (IC), or which genes contributed most strongly to one; those are findings of the citing study. (LLM, conf=0.86)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Across cell populations, expression of nAChR genes exhibited two prominent correlation blocks, each containing genes that encode subunits of known heteromeric α/β channels”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Gene-gene Co-expression Relationships Inferred from Hundreds of Cell types and States
What the sources say
- Partly supportedcites Zoli et al., 2015 sourceExperimental — accuracy not yet measured
Section 2, “Structure of nicotinic receptor subtypes” (page unavailable), says nAChR genes encode α and β subunits that form heteromeric receptors. It does not report an analysis of gene-expression correlations across cell populations or identify two correlation blocks; searches for those terms found no such passage. (LLM, conf=0.84)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Of the remaining subclusters, 3–12 expressed Slc17a6±, likely corresponding to the glutamatergic projection to thalamus (Antal et al., 2014).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
What the sources say
- Supportedcites Antal et al., 2014 source
The Results report that vGluT2-positive substantia nigra neurons provide glutamatergic output to the nRT and PTh thalamic nuclei. This supports the cited projection used to interpret the citing authors’ subcluster. (LLM, conf=0.88)
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Border-spanning subclusters may explain a neuron type that is synaptically incorporated into the GPe, but exhibits VP-like axonal projections (Chen et al., 2015; Saunders et al., 2015).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
What the sources say
- Partly supportedcites Chen et al., 2015 sourceExperimental — accuracy not yet measured
The Results section, ‘Striatal projections to pallidocortical neurons’ (page unavailable), shows that cortically projecting GPe neurons receive striatal input, supporting their incorporation into GPe circuitry. The Discussion (page unavailable) describes GPe and nearby basal-forebrain projecting neurons as appearing continuous in retrograde tracing, but it does not identify border-spanning subclusters or a neuron type with VP-like axonal projections. (LLM, conf=0.82)
- Partly supportedcites Saunders et al., 2015 sourceExperimental — accuracy not yet measured
The paper reports GP-to-frontal-cortex neurons clustered along GP borders and receiving synaptic input characteristic of GP circuitry (Abstract; page not specified). It does not describe their axonal projections as VP-like or propose border-spanning subclusters as an explanation for that combination. (LLM, conf=0.84)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Several subclusters (2–15, 2–21, and 2–18) expressed markers in the VP and bordering GPe (Figure 6D,E), suggesting neuron types shared across regions with different behavioral functions and connectivity (Gittis et al., 2014; Kita, 2007; Smith et al., 2009).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
What the sources say
- Supportedcites Gittis et al., 2014 source
The citation supports the background point about regional differences, not the citing authors’ own inference that their subclusters represent shared neuron types. In “Neuronal and functional studies of the pallidostriatal pathway” (page unavailable), the paper describes different inputs to the GPe and ventral pallidum; “Anatomical circuitry of distinct GPe functional territories” (page unavailable) describes distinct behavioral outputs and connections. (LLM, conf=0.88)
- Not checked — no full text of the sourcecites Kita, 2007 source
- Supportedcites Smith et al., 2009 source
In “Ventral Pallidum Function: Moving Beyond Movement” (page unavailable), the paper describes the VP as having distinctive connectivity and a reward-and-motivation role, contrasting it with the globus pallidus and its association with movement circuitry. The subclusters and the inference that neuron types are shared are the citing authors’ own observation, not a finding attributed to this source. (LLM, conf=0.85)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Only Pvalb and Penk/Foxp2 were selectively expressed in GPe subclusters (Kita, 1994; Voorn et al., 1999), suggesting 2–14 represents fast-spiking “prototypical” neurons, while 2–19 represents the slow-firing “arkypallidal” population (Abdi et al., 2015; Mallet et al., 2012).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
What the sources say
- Partly supportedcites Abdi et al., 2015 sourceExperimental — accuracy not yet measured
In the section on firing rates and patterns in dopamine-intact rats (Fig. 6), the paper identifies PV-positive prototypic GPe neurons and reports that they fire at relatively high rates. It does not identify subcluster 2–14; assigning that subcluster to this cell type is the citing authors’ inference. (LLM, conf=0.84)
- Partly supportedcites Mallet et al., 2012 sourceExperimental — accuracy not yet measured
The Discussion identifies predominantly parvalbumin-positive GP-TI neurons as prototypic, and the Introduction describes prototypic GPe neurons as fast-firing. The paper does not identify subcluster 2–14, so the assignment of that subcluster to this cell type is the citing authors’ inference, not a finding verified by this source. (LLM, conf=0.84)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Only Pvalb and Penk/Foxp2 were selectively expressed in GPe subclusters (Kita, 1994; Voorn et al., 1999), suggesting 2–14 represents fast-spiking “prototypical” neurons, while 2–19 represents the slow-firing “arkypallidal” population (Abdi et al., 2015; Mallet et al., 2012).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
What the sources say
- Supportedcites Abdi et al., 2015 source
The molecular-signatures section identifies FoxP2-expressing, PPE-expressing GPe neurons as arkypallidal. The in vivo firing-results section reports that identified FoxP2-positive neurons fired at low rates. The designation of subcluster 2–19 is the citing authors’ inference, rather than a designation attributed to Abdi et al. (LLM, conf=0.90)
- Partly supportedcites Mallet et al., 2012 sourceExperimental — accuracy not yet measured
The Results (Physiological Dichotomy; page unavailable) report that GP-TA neurons fired more slowly than prototypic GP-TI neurons during slow-wave activity, and the Discussion identifies GP-TA neurons as arkypallidal. The source does not identify subcluster 2–19, and the firing-rate distinction is condition-dependent: GP-TA neurons fired faster than GP-TI neurons during beta oscillations. (LLM, conf=0.85)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Subsets of the eSPN class share markers previously reported to distinguish SPN subtypes (Gökce et al., 2016).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > “Eccentric” SPNs: A novel, third axis of SPN diversity
What the sources say
- Supportedcites Gökce et al., 2016 source
The Results section identifies markers distinguishing D1 and D2 medium spiny neurons and reports subpopulations that coexpress markers of both types. This supports the citation’s role as earlier work identifying SPN subtype markers; the observation about eSPN subsets belongs to the citing study. (LLM, conf=0.90)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Expression of markers associated with canonical SPNs suggests eSPNs have been molecularly “camouflaged,” including in studies using mice that have employed Drd1 and Adora2a driven–transgenes to label and manipulate dSPNs or iSPNs (Heiman et al., 2008; Kozorovitskiy et al., 2012; Kravitz et al., 2012).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > “Eccentric” SPNs: A novel, third axis of SPN diversity
What the sources say
- Partly supportedcites Heiman et al., 2008 sourceExperimental — accuracy not yet measured
In Results (“The BACarray approach and methodology”; page unavailable), Heiman et al. describe D1-driven BAC transgenic mice used to identify striatonigral neurons. Results (“BACarray profiling of striatonigral and striatopallidal MSNs”; page unavailable) identify Adora2a as a striatopallidal marker, but the study uses D2—not Adora2a—to drive its striatopallidal transgene. It does not establish the cited example of Adora2a-driven transgenes used to label or manipulate those neurons; the molecular-camouflage inference is the citing authors’ framing. (LLM, conf=0.85)
- Partly supportedcites Kozorovitskiy et al., 2012 sourceExperimental — accuracy not yet measured
The paper’s indexed text (section labeled Abstract; page unavailable) describes using Drd1a-Cre mice to target direct-pathway MSNs and Adora2a-Cre mice as a driver line for indirect-pathway MSNs. It does not discuss eSPNs, canonical marker expression, or molecular camouflage, so it supports the cited example of transgenic mouse studies but not the sentence’s broader inference. (LLM, conf=0.86)
- Supportedcites Kravitz et al., 2012 source
The Methods (Subjects) identify D1-Cre and A2A-Cre transgenic mouse lines, and the Methods (Viral expression) describe Cre-dependent expression used to manipulate neurons. The citation supports identifying Kravitz et al. as one of the mouse studies described; molecular “camouflage” is the citing authors’ interpretation, not a finding attributed specifically to this study. (LLM, conf=0.90)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
- It may state two separate claims as one. 46
- Probably not a substantial claim. 28
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In the hippocampus, Pvalb± interneurons exhibited high cell loading for the Neurofilament IC, as did the Pvalb± “prototypical” neurons of the GPe”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > A Neuronal Transcriptional Program Related to Axon Function
What the sources say
- Supportedcites Mallet et al., 2012 source
The Results section, “Physiological Dichotomy Is Mirrored by Molecular Dichotomy” (page unavailable), reports that prototypic GP-TI neurons include both PV+ and PV− cells. The Results section on structural dichotomy identifies GP-TI neurons as prototypic GPe neurons. The Neurofilament IC loading is the citing authors’ observation, not a finding attributed to this source. (LLM, conf=0.90)
- Partly supportedcites Saunders et al., 2016 sourceExperimental — accuracy not yet measured
The Introduction (page unavailable in our copy) identifies PV-positive neurons of the external globus pallidus as a prototypic class. It does not report Neurofilament IC cell loading or a comparison with hippocampal interneurons, so those findings are not supported by this source. (LLM, conf=0.88)
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“We found that individual ICs corresponded to recognizable biological phenomena (Figure 1D and Figure S2C–H)(Adamson et al., 2016), in contrast to results from principal components analysis (Figure S2A).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Inference of Cell Types and States Using Independent Components Analysis
What the sources say
- Supportedcites Adamson et al., 2016 source
The Results section reports that independent components tracked PERK, IRE1α, and ATF6 perturbations or cell-cycle phase—recognizable biological phenomena. The PCA contrast points to the citing authors’ Figure S2A, so it is not a result attributed to Adamson et al.; the source’s STAR Methods also distinguishes ICA from PCA. (LLM, conf=0.90)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Subcluster 6 exhibited exclusive expression of Cytl1 and enrichment for arterial marker Bmx, along with other genes implicated in growth-factor dependent vascular remodeling”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Characteristics of the Cells of the Blood-Brain Barrier
What the sources say
- Not checked — no full text of the sourcecites Boström et al., 2004 source
- Partly supportedcites Contois et al., 2012 sourceExperimental — accuracy not yet measured
The section headed “IGFBP-4 Inhibits IGF-1 and FGF-2, but Not VEGF-induced Angiogenesis” reports that IGFBP-4 affects growth-factor-induced blood-vessel formation, supporting the general vascular-remodeling connection. Our copy does not report endothelial subcluster 6, exclusive Cytl1 expression, or enrichment of Bmx as an arterial marker. No page number is available in the indexed passages. (LLM, conf=0.86)
- Not matchedcites Fu et al., 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Our data suggest human neurological symptoms associated with Slc6a8 mutations (van de Kamp et al., 2013) may in part be due to deficits in fast-firing neurons, consistent with cortical GABAergic synapse loss observed in mouse models (Baroncelli et al., 2017).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Gene-gene Co-expression Relationships Inferred from Hundreds of Cell types and States
What the sources say
- Supportedcites van de Kamp et al., 2013 source
The citation to van de Kamp et al. is attached to the association between SLC6A8 mutations and human neurological symptoms. The Abstract (p. 1) reports intellectual disability, delayed speech, behavioural problems and seizures in affected patients. The proposed fast-firing-neuron mechanism is framed as the citing authors’ own suggestion, while the mouse-model finding has a separate citation. (LLM, conf=0.93)
- Not matchedcites Baroncelli et al., 2017
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“All these lines of evidence suggest that regional specializations are driven by glutamatergic neurons, consistent with theory from humans and other primates (Krienen et al., 2016).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Cell-type Specialization Between Cortical Poles
What the sources say
- Partly supportedcites Krienen et al., 2016 sourceExperimental — accuracy not yet measured
The Significance section (page unavailable) links supragranular pyramidal neurons to cortical connections in humans and other primates, and the Discussion reports regional differences in gene-expression profiles associated with connectivity. But the paper presents molecular mediation as a possibility; it does not establish that glutamatergic neurons drive regional specializations. (LLM, conf=0.82)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Differentially expressed genes exhibited >2 fold differences and had P<10−100 based on the binomTest (Robinson et al., 2010) and were visualized in SPN scatterplots with large, dark dots.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
What the sources say
- Supportedcites Robinson et al., 2010 source
The citation is attached to the testing method, not to the citing authors’ SPN results or their numerical thresholds. The source’s Model section (page not provided) describes edgeR’s exact test for differential expression, and its Features section says the test supports pairwise comparisons. Our copy does not name the function binomTest, but it supports the methodological attribution. (LLM, conf=0.84)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Neurofilament ICs were ascertained in all brain regions and appeared to shape gene expression in diverse neuronal populations.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > A Neuronal Transcriptional Program Related to Axon Function
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of these subclusters, 323 were neuronal, derived from 368 biological ICs (Data S3).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Inference of Cell Types and States Using Independent Components Analysis
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“A single IC (IC 13) appeared to encode this distinction, with pericyte marker Vtn as the strongest loading gene”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Characteristics of the Cells of the Blood-Brain Barrier
What the sources say
- Not checked — no full text of the sourcecites Vanlandewijck et al., 2018 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While endothelial subpopulations had similar abundances across regions, fibroblast-like subpopulations exhibited different contributions from cortical and subcortical areas”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Characteristics of the Cells of the Blood-Brain Barrier
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our data also identify new mural cell diversity. For example, cluster 1 expressed pericyte (Rgs5) and SMA (Acta2) markers.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Characteristics of the Cells of the Blood-Brain Barrier
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We found 7 subclusters each of endothelial (n=16,248) and fibroblast-like (n=1,587) cells”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Characteristics of the Cells of the Blood-Brain Barrier
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This adult mouse brain cell atlas, accessible through interactive online software (DropViz), serves as a reference for development, disease, and evolution.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In frontal cortex interneuron cluster 2, expression levels of the genes strongly contributing to the Neurofilament IC – including Nefh, Kcnc3, Syt2, and Nefm – were continuously distributed and strongly correlated among Pvalb+ interneurons but lower and less correlated among Sst+ interneurons”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > A Neuronal Transcriptional Program Related to Axon Function
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the globus pallidus externus (GPe) and substantia nigra reticulata (SNr), where neuron types are not well-understood, we propose complete neuron-type classifications and identify selective markers for each population.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the cortex and hippocampus contributed disproportionately to the population that more strongly expressed genes with membrane-transport functions (subcluster 6), while collagen-expressing cell populations (subclusters 2 and 3) came largely from the basal ganglia and thalamus”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Characteristics of the Cells of the Blood-Brain Barrier
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“we developed an interactive analysis platform (“DropViz”, http://dropviz.org/) for comparing cell types, identifying cell populations that express genes of interest, and performing many other kinds of analyses.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Introduction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This collection contained Neurofilament genes (Figure 3), ion channels (e.g. Hcn2), and unexpected genes, such as the transcription factor Foxj1 and the creatine transporter Slc6a8.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Gene-gene Co-expression Relationships Inferred from Hundreds of Cell types and States
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Expression of other gene pairs encoding heteromeric receptors (Chrna6/Chrnb3 and Chrna4/Chrnb2) was also well correlated, whereas Chrnb1 and Chrnb2 were not, consistent with a lack of β1/β2 channels in brain”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Gene-gene Co-expression Relationships Inferred from Hundreds of Cell types and States
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Subcluster 3–2 was Gad2+/Pvalb− and expressed the developmental marker Zfpm2 (Lahti et al., 2016), likely representing a third GABAergic type marked by Sox14 and Cplx3 expression (Figure 6I).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
What the sources say
- Not matchedcites Lahti et al., 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fast-firing cell types expressed high levels of Kcnc2/Kcnc1 and Scn1a/Scn8a, variable levels of Kcnc4, and low levels of Scn2a1, whose expression was inversely correlated with Kcnc1–3 and Scn1a”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Gene-gene Co-expression Relationships Inferred from Hundreds of Cell types and States
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The fourth GPe subcluster (2–17) is enriched for Elfn1/Grik3 and has not been characterized.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Two subcluster pairs shared selective markers (3–18/3–19: Sema3a, Adarb2; 3–17/3–13: Pax5, Pou6f2), suggesting relatedness, but were distinguished by ion channels (Kcnc3, Kcna1), transmembrane proteins (Tmem132c), and transcription factors (Pou3f1) that could imply a state or subtype distinction (Figure 6I).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
What the sources say
- Not matchedcites Figure 6I
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Neurons were over-represented relative to non-neurons (Drop-seq: 0.76 ± 02 mean±sem; tissue: 0.51 ± 03) and exhibited a twofold greater GABAergic/glutamatergic ratio (ISH: 5.1:1; Drop-seq: 11:1).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Cell-type Specialization Between Cortical Poles
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Other clusters contained hippocampal (cluster 1, n=73 cells), thalamic (cluster 2, n=297), and dopaminergic (cluster 4, n=1,841) populations.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Glutamatergic neurons exhibited unusual regional specialization not only in subpopulation representation (subclusters), but in the precise gene expression pattern within each subpopulation: far more genes were differentially expressed when comparing regions within glutamatergic neuron as opposed to interneuron or non-neuronal subpopulations”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Cell-type Specialization Between Cortical Poles
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In the paper
“Interestingly, 2–13 is similar to 2–14, sharing markers like Grem1, but is distinguished by stronger expression of Scn4b, Kcnc3 and other “Neurofilament program” genes (Data S5).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
What the sources say
- Not matchedcites Data S5
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Among GABAergic interneurons, Vip+ cells were over-represented and Sst+ and Pvalb+ cells were underrepresented (ISH vs Drop-seq: Vip+, 16% vs 35%; Pvalb+, 31% vs 25%; Sst+, 28% vs 22%), which cannot be explained by higher transcript counts (Pvalb+: 2,996 ± 61; Sst+: 2,758 ± 53; Vip+: 2,236 ± 32), suggesting a preferential depletion”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Cell-type Specialization Between Cortical Poles
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Glutamatergic neurons generated more regionally specialized ICs than GABAergic interneurons and non-neurons (Figure 5A), and exhibited subclusters with skewed regional abundances (Figure 5B–D).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Cell-type Specialization Between Cortical Poles
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For voltage gated Na and K alpha subunit analysis, only the 323 profiles corresponding to neurons were used”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Drop-seq profiles from six classes—excitatory neuron (n=82,936 cells), inhibitory neuron (n=7,783), oligodendrocyte/polydendrocyte (n=3,505), microglia/macrophage (n=1,027), astrocyte (n=7,782), and fibroblast-like/endothelial (n=3,578) — were individually extracted from the Frontal and Posterior Cortex DGEs”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A total of 1,758 ICs, distributed across 11 cell classes, were then individually manually curated for inclusion in clustering.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Here, we analyzed RNA expression in more than 690K individual cells sampled from nine different regions of the adult mouse brain, encompassing all brain cell classes.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Discussion
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Each analysis identified a single candidate “patch IC” whose most strongly contributing genes included known patch markers such as Tac1 and Pdyn.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Molecular Specializations of Striatal Principal Neurons
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“- Outlier (n=99), in which only a small number of cells (less than five) showed high cell scores;”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Strength:plain· not yet calibratedn = 99Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“While aspects of DA diversity related to spatial positioning – for example, delineating the dorsal (IC 10) from ventral (IC 12) VTA (Figure 6L–N) - cholinergic neurons exhibited only a Neurofilament-like signal (IC 4), with no spatial component (Figure 6O,P).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
What the sources say
- Not matchedcites Figure 6O,P
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“- Doublet (n=759), in which top loading genes were identified as markers of another cell class (e.g. microglial genes seen in the analysis of a neuronal cell class);”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Strength:plain· not yet calibratedn = 759Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“We observed classic (Tac1) and undescribed (Tshz1) pan-patch markers (Figure S7D) as well as genes enriched in either iSPN (Asic4) or dSPN (Necab1) patches (Figure S7E).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Molecular Specializations of Striatal Principal Neurons
What the sources say
- Not matchedcites Figure S7D
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Figure S7E
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“- Biological (n=601), in which the IC signal is believed to be likely biologically meaningful (or cannot be confidently assigned to categories 1, 2, or 3).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We also resolved signals from striatal SPNs representing differences in pathway (“direct” versus “indirect”), spatial arrangement (“patch” versus “matrix”), and a cryptic molecular SPN distinction (“eccentric” SPNs).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Discussion
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“- Artifact (n=315), in which either: (a) cell scores showed obvious replicate-specific biases, or (b) the top loading genes largely intersected with those observed to vary strongly with digest time (Figures 3 and S3), or contained many heat-shock proteins;”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Strength:plain· not yet calibratedn = 315Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Two large clusters corresponded to dSPNs (Cluster 10, n=30,835 cells) and iSPNs (Cluster 11, n=25,305)(Striatum dataset, Figure 7A,B) and were distinguished by 68 differentially expressed genes, including known and undescribed pathway markers (Figure 7C).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Molecular Specializations of Striatal Principal Neurons
What the sources say
- Not matchedcites Figure 7C
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We identified categorically distinct patterns of RNA expression originating from cell types, but also continuously varying patterns that appeared to correspond to spatial locations and cellular states.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Discussion
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We generated subcluster-level profiles by summing the integer counts of each cell in each subcluster together to form 565 distinct profiles (Figure S7A,B).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Here we developed techniques, borrowing ideas from preparations for electrophysiological recordings, that allowed adult brain tissue to be dissociated into intact cell bodies while representing all major cell classes.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Introduction
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These examples identify endothelial processes within (e.g. arterial angiogenesis) or across (e.g. host defense) vessel types. Other signals might reflect cell states specialized for iron handling, calcium signaling, and the stress response”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Characteristics of the Cells of the Blood-Brain Barrier
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Genes contributing to this transcriptional pattern appear to maintain axon function and support or tune neurotransmitter release.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > A Neuronal Transcriptional Program Related to Axon Function
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, IC 10 loads onto cells with both artery and vein markers and strongly contributing genes suggest a signal related to host immunity”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Characteristics of the Cells of the Blood-Brain Barrier
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These examples suggest that fibroblast-like cells are transcriptionally specialized to support membrane transport and ECM production; ECM secretion may involve different sets of co-regulated collagen genes.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Characteristics of the Cells of the Blood-Brain Barrier
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These experiments confirm that the Neurofilament signal is present in vivo and not an artifact of cell isolation.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > A Neuronal Transcriptional Program Related to Axon Function
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The GPe and SNr abut populations of neuromodulatory neurons that release acetylcholine (ACh) or dopamine (DA), respectively.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Rests on an assumption: The GPe and SNr lie next to neuromodulatory populations releasing acetylcholine or dopamine, respectively.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Molecular specializations related to anatomical location exist for DA neuron subtypes (Lammel et al., 2008; Poulin et al., 2014), yet it is unclear if ACh-releasing neurons exhibit similar heterogeneity and whether this diversity has a spatial component.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
What the sources say
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Neurons of the striatum have been extensively characterized, yet neurons of the globus pallidus externus (GPe) and substantia nigra reticulata (SNr) have received less attention (Figure 1 & Data S1).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
What the sources say
- Not matchedcites Figure 1 & Data S1
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Not matched
In the paper
“Two subcluster pairs shared selective markers (3–18/3–19: Sema3a, Adarb2; 3–17/3–13: Pax5, Pou6f2), suggesting relatedness, but were distinguished by ion channels (Kcnc3, Kcna1), transmembrane proteins (Tmem132c), and transcription factors (Pou3f1) that could imply a state or subtype distinction (Figure 6I).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Resolving Neuron Types within the Basal Ganglia
What the sources say
- Not matchedcites Figure 6I
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“At the same time, the magnitude of expression varied among neurons of the same subtype, suggesting that this transcriptional program contributes to both intra- and inter-type diversity.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > A Neuronal Transcriptional Program Related to Axon Function
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“for example, Kcnc4 could be a Kv3 family member that tailors membrane properties orthogonal to firing frequency, and Scn2a1 might undermine fast-firing.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Gene-gene Co-expression Relationships Inferred from Hundreds of Cell types and States
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The first – based on divergent axonal projections and dopamine signaling – assigns SPNs to similarly numerous “direct” (dSPN) and “indirect” (iSPN) pathways (Albin et al., 1989).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Molecular Specializations of Striatal Principal Neurons
What the sources say
- Not checked yetcites Albin et al., 1989 source
Checked before — its earlier verdict is held back until a new check runs.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not checked yet
In the paper
“One clue about the anatomical identity of eSPNs comes from this small Th+ population, as spiny Th+ principal cells with similar spatial arrangement to eSPNs have been observed in striatum and appear to be dynamically regulated by dopamine (Darmopil et al., 2008).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > “Eccentric” SPNs: A novel, third axis of SPN diversity
What the sources say
- Not checked — no full text of the sourcecites Darmopil et al., 2008 source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To calculate the regional skew of ICs (Figure 5A), cells from each region were downsampled to equal representation”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To enable diverse uses of our atlas, we developed interactive web-based software (DropViz, http://dropviz.org/) that facilitates access and dynamic exploration of the data.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Discussion
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“In almost all instances of subclustering, this value was used, with a few exceptions where K was increased slightly.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: The independent component space is highly dependent on the number of components K selected for computation.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“By studying gene loadings and cell scores, each IC was assigned to one of four “status” categories:”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“The second – based on processing limbic versus sensory/motor information – groups SPNs into spatial compartments, the so-called “patch” and “matrix” (Gerfen, 1992; Graybiel and Ragsdale, 1978).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > Results > Molecular Specializations of Striatal Principal Neurons
What the sources say
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Cells with fewer than 400 expressed genes were removed from analysis.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Only ICs annotated as Biological ICs were included in the generation of the SNN graph for clustering; Technical ICs (Doublet, Outlier, and Artifact) were not included.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Non-eSPN subclusters (13–6, 13–7, 13–8, 13–9 and 13–10) were excluded from the differential expression analysis described in Figure S7I.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Doublets and Outliers were identified as cells whose upper-bound p-value was less than 0.01 (FDR-corrected).”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All experiments were approved by and in accordance with Harvard Medical School IACUC protocol number IS00000055–3.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > EXPERIMENTAL MODEL AND SUBJECT DETAILS
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Subclusters were classified as having FC versus PC skew if they 1) exhibited >3:1 compositional difference between FC and PC and 2) P < 0.05 (Bonferroni-adjusted) using Barnard’s exact test.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“we first calculated the average mean and variance of each gene, and selected genes that were: (1) 0.1 log10 units above the expected variance for a perfectly Poisson-distributed gene of equivalent mean expression; and (2) above a Bonferroni-corrected 99% confidence interval defined by a normal approximation of a Poisson distribution.”
§ Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain > STAR Methods > QUANTIFICATION AND STATISTICAL ANALYSIS
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
The paper relies on this without support.
The nine sampled brain regions are representative of the adult mouse brain.
“sampled from nine regions of the adult mouse brain”
“from nine brain regions”
The paper relies on this without support.
Transcriptional clusters derived from single-cell RNA-seq correspond to biologically meaningful cell types or states.
“We identified 565 transcriptionally distinct groups of cells”
“grouped cells based on combinations of “biological IC” cell loadings into 565 subclusters”
The paper relies on this without support.
About 95% of rodent striatal neurons are spiny projection neurons.
“Spiny projection neurons (SPNs) represent ~95% of neurons in rodent striatum.”
The paper relies on this without support.
dSPNs and iSPNs both occur in patch and matrix compartments.
“Both dSPNs and iSPNs are present in the patch and matrix.”
The paper relies on this without support.
The GPe and SNr lie next to neuromodulatory populations releasing acetylcholine or dopamine, respectively.
“The GPe and SNr abut populations of neuromodulatory neurons that release acetylcholine (ACh) or dopamine (DA), respectively.”
The paper relies on this without support.
The independent component space is highly dependent on the number of components K selected for computation.
“The independent component space is highly dependent on the number of components K that are selected for computation.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Our inhibitor-based protocol offers much greater experimental and tissue-/cell-type flexibility than previously developed experimental and computational solutions for this pervasive issue (see additional discussion of some alternative methods in Supplementary Note 7)33,34,45,65,67,68.”
From Dissection of artifactual and confounding glial signatures by single-cell sequencing of mouse and human brain (2022) · cites this paper as [33]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Virtually identical expression patterns are observed in the DropViz adult mouse brain atlas, where the top ten Plcg2-expressing clusters are classified as microglia/macrophages and endothelial cells, with the strongest expression in microglia/macrophage clusters marked by Tmem119-Mrc1 and C1qb [73].”
From PLCγ2 deficiency compromises systemic immune tolerance and erodes myelin homeostasis while enhancing oxidative metabolism in the mouse brain (2026) · cites this paper as [73]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“This astrocyte-specific expression of Apoe in wildtype adult mouse brain was independently confirmed in a publicly accessible single-cell RNA-seq database (Saunders et al. 2018) (Fig. S1).”
From Apolipoprotein E ε4 disrupts oligodendrocyte differentiation by interfering with astrocyte‐derived lipid transport (2023) · cites this paper as Saunders et al. 2018
The cited source says what the claim attributes to it.
In the citing paper
“We found that the thalamic–neocortex separation is conserved in mice and recapitulated both this divide and thalamic marker genes in independent single-cell, bulk and microarray data in both mice and humans8,39,40,43”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [39]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Apoe Astrocyte_Gja1 [#8] 7.537 0.0E+00”
From Apolipoprotein E ε4 disrupts oligodendrocyte differentiation by interfering with astrocyte‐derived lipid transport (2023) · cites this paper as Saunders et al. 2018
Experimental — accuracy not yet measured
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“In the Aging Mouse Brain Single Cell Portal, Plcg2 expression is highest in microglia and brain macrophages, with lower levels in endothelial cells and oligodendrocyte precursor cells and minimal to no expression in neurons, astrocytes, and mature oligodendrocytes [30, 72–74].”
From PLCγ2 deficiency compromises systemic immune tolerance and erodes myelin homeostasis while enhancing oxidative metabolism in the mouse brain (2026) · cites this paper as [73]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Notably, the thalamic MEIS2+FOXP2+ interneurons shared multiple markers with neocortical GRM3 astrocytes, including GRM3, MEIS2 and VAV339,40,43”
From Single-cell multiregion dissection of Alzheimer’s disease (2024) · cites this paper as [39]
Experimental — accuracy not yet measuredIn the citing paper
“Ank2 encodes an ankyrin protein and is expressed broadly in excitatory and inhibitory neurons as well as glial cells in the brain (23).”
From In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes (2020) · cites this paper as [23]
In the citing paper
“Ank2 encodes an ankyrin protein and is expressed broadly in excitatory and inhibitory neurons as well as glial cells in the brain (23).”
From In vivo Perturb-Seq reveals neuronal and glial abnormalities associated with autism risk genes (2020) · cites this paper as [23]
In the citing paper
“For both rounds of analysis, independent components that captured batch or replicate effects were removed before clustering analysis (Fig S2d), as previously described (Saunders et al., 2018).”
From Complex cell-state changes revealed by single cell RNA sequencing of 76,149 microglia throughout the mouse lifespan and in the injured brain (2018) · cites this paper as Saunders et al., 2018
In the citing paper
“Recent advances in the collection and analysis of single cell RNA-sequencing (scRNAseq) data provide a promising new way to define cell types and how they relate to one another”
From Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells (2020) · cites this paper as Saunders et al., 2018
In the citing paper
“Comparing this taxonomy with six previous studies (Cembrowski et al., 2018; Harris et al., 2018; Saunders et al., 2018; Tasic et al., 2018; Yao et al., 2020; Zeisel et al., 2018), we found generally good but variable correspondences”
From A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation (2021) · cites this paper as Saunders et al., 2018
In the citing paper
“First, high-resolution and high-coverage omics data sets are needed to provide the necessary substrates for comprehensive analysis and discovery. Large-scale, lower-resolution data sets alone (for example, assessing the expression on the order of 2,000 genes per cell71,72,73) may preclude the delineation of functional gene expression signatures.”
From The diversity of GABAergic neurons and neural communication elements (2019) · cites this paper as [72]
In the citing paper
“The application of single-cell RNA sequencing (scRNA-seq) to the neocortex and other brain regions has revealed a complex but tractable hierarchical organization of transcriptomic cell types that are consistent overall with knowledge from decades of anatomical, physiological and developmental studies but with an unmatched level of granularity7–11.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [8]
In the citing paper
“An identical procedure was used for the promoter region chromatin accessibility scores × cell-type counts matrix starting with the annotated set of 310 marker genes from a previous scRNA-seq analysis of mouse brain29, resulting in 262 genes for which the zi,j > 3 criterion was met for the promoter gene scores (Fig. 2d).”
From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [29]
In the citing paper
“we used a resource containing scRNA-seq data for 690,000 individual cells sampled from nine regions of the adult mouse brain29, which identified 565 subclusters within the broad classes of cell types in the brain.”
From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [29]
In the citing paper
“we found that the relative numbers of interneuron subtypes that we measured from the single-nucleus RNA-seq experiments were similar to those measured with stereology, without the selection biases introduced by efforts to capture intact cell soma for single-cell RNA-seq15”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [15]
In the citing paper
“primate cortical LAMP5+ LHX6+ cells most closely resembled Lamp5+ Lhx6+ interneurons in the mouse hippocampus”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [15]
In the citing paper
“the marmoset striatum revealed–in addition to all the major classes of striatal interneurons found in mice15,42–an abundant, transcriptionally distinct interneuron type that did not appear to have a molecularly homologous population among mouse striatal interneurons”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [15]
In the citing paper
“which suggests that the genes that encode these markers stably delineate a core repertoire of neocortical interneuron types in these species11,15,18”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [15]
In the citing paper
“Joint analysis of the marmoset striatal interneurons with mouse interneurons from eight brain regions15 found no homologous cell population in any of the mouse regions sampled”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [15]
In the citing paper
“gene-expression patterns for these conserved types differed by cortical region in marmosets”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [15]
In the citing paper
“Homologous cell types can acquire species-specific functions through changes in gene expression9,15,17.”
From Innovations present in the primate interneuron repertoire (2020) · cites this paper as [15]
In the citing paper
“A second population of our nuclei aligned to cluster 3, containing the striatal eSPNs we recently described (Saunders et al., 2018).”
From Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity (2019) · cites this paper as Saunders et al., 2018
In the citing paper
“We found that a cluster annotated as “deep layer cluster 3” aligned uniquely to an RNA-seq cluster that we previously had annotated as claustrum (Saunders et al., 2018) (Figures 6C, D).”
From Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity (2019) · cites this paper as Saunders et al., 2018
In the citing paper
“Transcriptomically similar neuronal subpopulations (VIP/ChAT+) have also been identified with largescale single-cell sequencing of the motor and visual cortex in mouse, marmosets, and humans.6,[94][95][96]”
From Synaptic and circuit functions of multitransmitter neurons in the mammalian brain (2023) · cites this paper as [6]
In the citing paper
“These studies also confirmed that this cortical subtype expresses all the required genes for synthesis and release of both GABA and Ach.”
From Synaptic and circuit functions of multitransmitter neurons in the mammalian brain (2023) · cites this paper as [6]
In the citing paper
“These studies have also shown the ubiquity of neuron types that have the genetic constituents required for the release of more than one neurotransmitter.6,7,9,40,131”
From Synaptic and circuit functions of multitransmitter neurons in the mammalian brain (2023) · cites this paper as [6]
In the citing paper
“An important caveat to these findings is that most analyses performed in these studies are performed on populations of neurons.”
From Synaptic and circuit functions of multitransmitter neurons in the mammalian brain (2023) · cites this paper as [6]
In the citing paper
“Although these studies differed significantly in their experimental parameters and analysis methods, they identified partially overlapping subtypes, some of which were located in a manner agnostic to traditional anatomical boundaries (SN, VTA, & RR).”
From Classification of midbrain dopamine neurons using single-cell gene expression profiling approaches (2020) · cites this paper as [18]
In the citing paper
“Molecular heterogeneity within the murine DAergic system has been investigated by at least six independent groups, all attempting to classify DA neurons based on gene expression [13–18].”
From Classification of midbrain dopamine neurons using single-cell gene expression profiling approaches (2020) · cites this paper as [18]
In the citing paper
“In addition to lacking Aldh1a1 mRNA, they also do not express Anxa1 [14,17,18] and Aldh1a7 [14,17,18].”
From Classification of midbrain dopamine neurons using single-cell gene expression profiling approaches (2020) · cites this paper as [18]
In the citing paper
“Sox6+, Aldh1a1- DA neurons are located in the SNc, parabrachial VTA, and RR”
From Classification of midbrain dopamine neurons using single-cell gene expression profiling approaches (2020) · cites this paper as [18]
In the citing paper
“This group of cells is enriched for Vgat [13–15,18], Calb1 [13,14], Crhbp [15,18], Gad2 [15,18] and Wnt7b [15,18] mRNAs.”
From Classification of midbrain dopamine neurons using single-cell gene expression profiling approaches (2020) · cites this paper as [18]
In the citing paper
“Many of these neurons also express Vglut2 (a.k.a. Slc17a6), a glutamate vesicular transporter [13,18], and also have lower Dat and Th expression [15].”
From Classification of midbrain dopamine neurons using single-cell gene expression profiling approaches (2020) · cites this paper as [18]
In the citing paper
“These cells also co-express high levels of many of the mRNAs aforementioned in the previous population (Ndnf, Igf1 and Sncg), but in contrast, some of these neurons also express Calb1 [13,14], Lypd1 [14], Tacr3 [13,18], and Cyp26b1 [18] mRNAs.”
From Classification of midbrain dopamine neurons using single-cell gene expression profiling approaches (2020) · cites this paper as [18]
In the citing paper
“Vglut2+,Calb1+ DA neurons are ambiguous and likely encompass two or more distinct populations”
From Classification of midbrain dopamine neurons using single-cell gene expression profiling approaches (2020) · cites this paper as [18]
In the citing paper
“Examination of Htr1b expression in cortex, striatum, and ventral midbrain suggests that expression of this receptor in pvMΦs is unique to the DRN and its close surroundings (Hrvatin et al., 2018; Saunders et al., 2018; Zeisel et al., 2018).”
From Molecular and anatomical organization of the dorsal raphe nucleus (2019) · cites this paper as Saunders et al., 2018
In the citing paper
“prior studies that demonstrated prominent transcriptional differences between the two geometric poles of the cortex (i.e., anterior and posterior)47”
From Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction (2019) · cites this paper as [47]
In the citing paper
“in contrast to what has been seen in Alzheimer’s disease which shows a strong bias towards blood, spleen, lungs and microglial enrichments (39)”
From Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies (2019) · cites this paper as [39]
Citations
Cites
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“(Right) Cluster 13 versus tio >2 and p < 10 100 by binomTest (Robinson et al., 2010) (STARMethods) are ression in eSPNs.”
BackgroundUltrastructural Characterization of the Lower Motor System in a Mouse Model of Krabbe Disease
Cappello, V., Marchetti, L., Parlanti, P., Landi, S., Tonazzini, I., Cecchini, M., Piazza, V., & Gemmi, M. (2016) · Scientific Reports · doi:10.1038/s41598-016-0001-8 · Cited by 13,097
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · cited 1×
“High-throughput single-cell RNA sequencing (scRNA-seq) now enables RNA profiling in thousands of individual cells in complex tissue (Han et al., 2018; Klein et al., 2015; Macosko et al., 2015; Rosenberg et al., 2018; Zheng et al., 2017).”
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · cited 13×
“…AGATCTACACGCCT GTCCGCGGAAGCAGTGGTATCAACGCAGAGT* A*C Macosko et al., 2015 N/A Read1CustomSeqB: GCCTGTCCGCGGAAGCA GTGGTATCAACGCAG AGTAC Macosko et al., 2015 N/A Software and Algorithms Drop-seq_tools Macosko et al., 2015 http://mccarrolllab.com/dropseq/ IcaCluster This paper…”
InfluentialSelf-citationSpatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055 · cited 1×
“Inference of Cell Types and States Using Independent Components Analysis The recognition of subtle variation among cells of the same class presents a formidable analytical challenge for unsupervised analysis (Mayer et al., 2015; Satija et al., 2015; Shekhar et al., 2016; Tanay and Regev, 2017; Tasic et al., 2016).”
BackgroundFast and robust fixed-point algorithms for independent component analysis
Hyvärinen, A. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.761722 · Cited by 6,304 · cited 3×
“To automatically nominate a value for K, we took advantage of the fact that the fastICA algorithm begins with a whitening step, in which a singular value decomposition is used to select the top K eigenvectors (i.e., principal components) for maximization of non-Gaussianity (Hyvärinen, 1999).”
BackgroundMethodInfluentialAstrocyte-endothelial interactions at the blood-brain barrier
Abbott, N. J., Rönnbäck, L., & Hansson, E. (2006) · Nature Reviews Neuroscience · doi:10.1038/nrn1824 · Cited by 5,595 · cited 1×
“The first stage of analysis robustly grouped cells into 8–11 broad classes (Data S2), including neurons, astrocytes, microglia/macrophages, oligodendrocytes, polydendrocytes (oligodendrocyte progenitor cells), and components of the vasculature—endothelial cells, fibroblast-like cells, andmural cells (Abbott et al., 20…”
BackgroundThe functional anatomy of basal ganglia disorders
Albin, R. L., Young, A. B., & Penney, J. B. (1989) · Trends in Neurosciences · doi:10.1016/0166-2236(89)90074-x · Cited by 5,353 · cited 2×
“Here, we illustrate systematic neuron classification using three disease-relevant nuclei of the basal ganglia (Albin et al. 1989).”
MethodCellProfiler: image analysis software for identifying and quantifying cell phenotypes
Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O., Guertin, D. A., Chang, J. H., Lindquist, R. A., Moffat, J., et al. (2006) · Genome biology · doi:10.1186/gb-2006-7-10-r100 · Cited by 5,212
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
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A single-cell genomic atlas for the effects of chronic ethanol exposure in the mouse dorsal striatum
Wildermuth, E., Patton, M. S., Cortes-Gutierrez, M., Jinwala, Z., Grissom, B., Campbell, R. R., Kranzler, H. R., Lobo, M. K., Ament, S. A., & Mathur, B. N. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03014-z · Cited by 5
Jury‐Garfe, N., Chimal‐Juárez, E., Patel, H., Martinez‐Pinto, J., Vanderbosch, K., Mardones, M. D., Perkins, A., Prisco, G. V. D., Marambio, Y., Vidal, R., et al. (2025) · Alzheimer's & Dementia · doi:10.1002/alz.70238 · Cited by 5
“63 Alternatively, considering the evidence showing that tau is also expressed in astrocytes, microglia, and oligodendrocytes, 30,64 it is possible to suggest that the ablation of tau in these glial cells could contribute to the beneficial effect of global tau depletion observed in the Tg-FDD model…”
Stojanovic, S., Knowlton, C. J., Egger-Mackrodt, R., Mankel, J., Shin, J., Lammel, S., Canavier, C. C., & Roeper, J. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0361-25.2025 · Cited by 6
“…diversity of DA functions is realized on multiple scales ranging from gene expression pro fi les of individual DA neurons (Poulin et al., 2014, 2018; Saunders et al., 2018; Tiklová et al., 2019), cellular electrophysiology, and calcium dynamics (Lammel et al., 2008; Evans et al., 2017; Tarfa et…”
InfluentialZhang, C., Li, M., Sun, P., Hui, L., Gao, Y., Yang, J., Zhang, N., Feng, X., Wu, Y., Guo, L., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02147-2 · Cited by 7
“We detected enrichment in eMSNs within the caudate and amygdala, a novel subclass indistinguishable using classic D1/ D2 markers 45 , mirroring findings from both PGC4 GWAS and a recent study on schizophrenia 3,46 .”
Changes in the brain [NAD+]/[NADH] and [NADPH]/[NADP+] with aging and anti-aging dietary restriction
Jamerson, L. E., Bradshaw, T., & Bradshaw, P. C. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1689139 · Cited by 6
“Frontiers in Aging Neuroscience 10 frontiersin.org The higher gene expression of GPD2 than GPD1 and GPD1L in mouse brain (Supplementary Table 1; Saunders et al., 2018), together with the very low GPD1 and GPD1L protein levels in mouse brain compared to skeletal muscle, liver, or kidney (Liu et al.,…”
InfluentialCellular and signalling mechanisms that regulate the blood–brain barrier
Amick, J., Gordon, L., & Gu, C. (2026) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-026-01000-z · Cited by 12
“Heterogeneity along the vascular tree also manifests within the endothelium. scRNA-seq analyses of brain endothelial cells have uncovered distinct transcriptional profiles along the arteriovenous axis in both mice and humans 11,201–203 .”
Regio, S., Vachey, G., Goñi, E., Duarte, F. T., Rybarikova, M., Sipion, M., Rey, M. J., Huarte, M., & Déglon, N. (2023) · Brain Communications · doi:10.1093/braincomms/fcad344 · Cited by 5
“34,35 We then used the Augur pipeline to identify the cell types in the striatum most responsive to neuronal Htt inactivation (Fig.”
MethodBhojwani-Cabrera, A. M., Bautista‐García, A., Neubrand, V. E., Membrive‐Jiménez, F. A., Bramini, M., Martín‐Oliva, D., Cuadros, M. Á., Marín‐Teva, J. L., Navascués, J., Vangheluwe, P., et al. (2024) · Glia · doi:10.1002/glia.24528 · Cited by 6
“We failed to detect SPCA2 with specific antibodies (data not shown), which is in line with gene expression databases highlighting an almost complete absence of Atp2c2 transcripts in mouse microglia (Saunders et al., 2018), suggesting that SPCA1 represents the primary isoform in microglia.”
ResultC-ziptf: stable tensor factorization for zero-inflated multi-dimensional genomics data
Chafamo, D., Shanmugam, V., & Tokcan, N. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05886-4 · Cited by 6
Astrocytes phenomics as new druggable targets in healthy aging and Alzheimer’s disease progression
Lana, D., Ugolini, F., Iovino, L., Attorre, S., & Giovannini, M. G. (2025) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2024.1512985 · Cited by 6
Liang, M., Chu, L. M., & Yue, Z. (2025) · Movement Disorders · doi:10.1002/mds.30097 · Cited by 6
“17 New computational algorithms, such as SCE-NIC + 55 and Dictys, 56 are also effective tools for uncovering dynamic regulatory networks by integrating multiomics single-cell data, including chromatin accessibility, gene expression, and transcriptional factor binding.”
Jiang, D., Yang, L. C., Yu, L., Zhang, M., Zhou, Y., Wang, Y., Ruan, Y., Fei, F., & Chen, Z. (2025) · Epilepsia · doi:10.1111/epi.18299 · Cited by 6
“29 P-SNr mostly contains glutamic acid decarboxylase 2 (GAD2)- positive interneurons distributed in its medial part, 50 which suggests that the cellular constitution of the regions differs at the molecular level.”
Blankvoort, S., Olsen, L. C., & Kentros, C. G. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.806154 · Cited by 5
Applications of single‐cell multi‐omics sequencing in deep understanding of brain diseases
Zhang, X., Lü, P., & Shen, X. (2022) · Clinical and Translational Discovery · doi:10.1002/ctd2.95 · Cited by 6
Li, P., Wei, J., & Zhu, Y. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad417 · Cited by 5
“Collection and processing of single-cell RNA-seq datasets Human and mouse brain scRNA-seq or snRNA-seq datasets (45-73) were collected from publicly available resources, including GEO (https://www.”
Laighneach, A., Kelly, J. P., Desbonnet, L., Holleran, L., Kerr, D. M., McKernan, D. P., Donohoe, G. J., & Morris, D. W. (2023) · PLoS ONE · doi:10.1371/journal.pone.0295855 · Cited by 5
Assali, A., Chenaux, G., Cho, J. Y., Berto, S., Ehrlich, N. A., & Cowan, C. W. (2024) · Development · doi:10.1242/dev.201439 · Cited by 5
Kaiser, T., Dattero, J., Li, L., Chen, M., Jiang, M., Harrahill, A., Butovsky, O., & Feng, G. (2024) · eNeuro · doi:10.1523/eneuro.0549-23.2024 · Cited by 7
“Crucially, Fcrls is also not expressed in neurons (Bennett et al., 2016; Saunders et al., 2018).”
Enhlink infers distal and context-specific enhancer–promoter linkages
Poirion, O. B., Zuo, W., Spruce, C., Baker, C. N., Daigle, S., Olson, A., Skelly, D. A., Chesler, E., Baker, C. L., & White, B. S. (2024) · Genome Biology · doi:10.1186/s13059-024-03374-9 · Cited by 3
“We annotated these 18 cell clusters using marker genes identified through the FindAllMarkers() command through DropViz [48].”
MethodGoode, T. D., Bernstein, M. X., Totty, M. S., Alipio, J. B., Vicidomini, C., Pathak, D., Besnard, A., Chizari, D., Sachdev, N., Kritzer, M. D., et al. (2026) · Neuron · doi:10.1016/j.neuron.2026.01.025 · Cited by 8
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Schroeder, M. E., Bassett, D. S., & Meaney, D. F. (2022) · Network Neuroscience · doi:10.1162/netn_a_00227 · Cited by 5
Brain aging is faithfully modelled in organotypic brain slices and accelerated by prions
Liu, Y., Senatore, A., Sorce, S., Nuvolone, M., Guo, J., Gumus, Z. H., & Aguzzi, A. (2022) · Communications Biology · doi:10.1038/s42003-022-03496-5 · Cited by 5
In Vivo Optical Interrogation of Neuronal Responses to Genetic, Cell Type-Specific Silencing
Terzi, F., Knabbe, J., & Cambridge, S. B. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.2253-22.2023 · Cited by 5
Sequencing the Human Brain at Single-Cell Resolution
Maitra, M., Nagy, C., & Turecki, G. (2019) · Current Behavioral Neuroscience Reports · doi:10.1007/s40473-019-00192-3 · Cited by 5
Sequencing Diversity One Cell at a Time
Oldham, M. C., & Kreitzer, A. C. (2018) · Cell · doi:10.1016/j.cell.2018.07.024 · Cited by 5
“In this issue of Cell, Zeisel et al. (2018), Saunders et al. (2018), and Davie et al. (2018) take this approach to a new level by transcriptionally profiling hundreds of thousands of individual cells from mouse and fly brains (Figure 1).”
BackgroundMahadevan, V., Peltekian, A., & McBain, C. J. (2020) · Current Protocols in Neuroscience · doi:10.1002/cpns.93 · Cited by 5
“Internet Resources Dropviz—http://dropviz.org/ A publicly available scRNA-seq dataset compiling the transcriptomes of 690,000 individual cells sampled from nine regions of the adult mouse brain (Saunders et al., 2018).”
BackgroundCoordinating Receptor Expression and Wiring Specificity in Olfactory Receptor NeuronsPreprint
Li, H., Li, T., Horns, F., Li, J., Xie, Q., Xu, C., Wu, B., Kebschull, J. M., Vacek, D., Xie, A., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/594895 · Cited by 5
Metric learning enables synthesis of heterogeneous single-cell modalitiesPreprint
Singh, R., Hie, B., Narayan, A., & Berger, B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/834549 · Cited by 4
Cosgrove, D., Whitton, L., Fahey, L., Broin, P. Ó., Donohoe, G. J., & Morris, D. W. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.16.877837 · Cited by 5
Multitask learning for Transformers with application to large-scale single-cell transcriptomesPreprint
Pang, M., & Tegner, J. N. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.02.05.935239 · Cited by 5
“Because both of them are from the nervous system and there are many research projects ([3],[30],[31]) in this field, we can evaluate our findings by comparing with those results.”
ResultCoexpression enables multi-study cellular trajectories of development and diseasePreprint
Hie, B., Cho, H., Bryson, B. D., & Berger, B. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/719088 · Cited by 5
“and Saunders et al.(5) we only considered cells that 380”
BackgroundTranscriptional and Anatomical Diversity of Medium Spiny Neurons in the Primate StriatumPreprint
He, J., Kleyman, M., Chen, J., Alikaya, A., Rothenhoefer, K. M., Öztürk, B. E., Wirthlin, M. E., Bostan, A. C., Fish, K. N., Byrne, L. C., et al. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3904348 · Cited by 5
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Batch alignment of single-cell transcriptomics data using deep metric learningPreprint
Yu, X., Xu, X., Zhang, J., & Li, X. (2022) · Research Square · doi:10.21203/rs.3.rs-2049486/v1 · Cited by 6
Overlapping representations of food and social stimuli in VTA dopamine neuronsPreprint
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Sibling chimerism among microglia in marmosetsPreprint
del Rosario, R. C. H., Krienen, F. M., Zhang, Q., Goldman, M., Mello, C. J., Lutservitz, A., Ichihara, K., Wysoker, A., Nemesh, J. C., Feng, G., et al. (2024) · eLife · doi:10.7554/elife.93640 · Cited by 5
“Markers for each cluster were identified using differential gene expression analysis (Krienen et al., 2020; Saunders et al., 2018).”
InfluentialSelf-citationA Hypothalamic Circuit that Modulates Feeding and Parenting BehaviorsPreprint
Alcantara, I. C., Li, C., Mickelsen, L. E., Mazzone, C. M., Salgado, I. D. A., Gao, C., Papas, B. N., Xiao, C., Karolczak, E. O., Goldschmidt, A. I., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.22.604437 · Cited by 6
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Brain macrophages and pial fibroblasts promote inflammation in a hypomyelination model
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Co-Conservation of synaptic gene expression and circuitry in collicular neurons
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Self-citationStress resilience is associated with transcriptional remodeling in the VTA
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The axon initial segment-associated microglia regulate neuronal activity and visual perception
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“Furthermore, we have expanded our evaluation to include a triple-omics integration benchmark using single-cell RNA-seq, scATAC-seq,and DNA methylationdatafrommousecortex tissue, providing insights into DMs ’ performance in more complex multi-modalintegrationscenarios 5,50 .”
Glial cells in neuropathic pain
Berta, T., Khoutorsky, A., Xu, J., & Ji, R. (2026) · Physiological Reviews · doi:10.1152/physrev.00034.2025 · Cited by 7
An X-linked long non-coding RNA, PTCHD1-AS, and the core features of autism
Bradley, C. A., Ko, S. Y., Tian, M., Ralph, L. T., D’Abate, L., Lee, J., Liu, T., Wang, J., Tidball, P., de Aquino, M. M., et al. (2026) · Nature · doi:10.1038/s41586-026-10515-6 · Cited by 5
“Cluster annotation was done using DropViz annotations and partially supervised with published major markers for cell types.”
Role of the substantia nigra pars reticulata in sleep-wakefulness: A review of research progress
Liang, E., Chen, Y., Yan, Y., Wang, S., Yuan, J., & Yu, T. (2023) · Sleep Medicine · doi:10.1016/j.sleep.2023.11.035 · Cited by 4
Self-citationThe X-linked intellectual disability gene CUL4B is critical for memory and synaptic function
Jiang, W., Zhang, J., Wang, Μ., Zou, Y., Liu, Q., Song, Y., Sun, G., Gong, Y., Zhang, F., & Jiang, B. (2024) · Acta Neuropathologica Communications · doi:10.1186/s40478-024-01903-y · Cited by 4
METTL9 sustains vertebrate neural development primarily via non-catalytic functions
Codino, A., Spagnoletti, L., Olobardi, C., Cuomo, A., Santos-Rosa, H., Palomba, M., Margaroli, N., Girotto, S., Scarpelli, R., Luan, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62414-5 · Cited by 5
“Furthermore, we con fi rmed that Mettl9 is highly expressed in the direct and indirect spiny projection neurons of the mouse striatum, as shown by re-analysis of available scRNA-seq data 27 (Supplementary Fig.”
Moussa, A. J., & Wester, J. C. (2022) · Frontiers in Neural Circuits · doi:10.3389/fncir.2022.982721 · Cited by 6
“In contrast, the transcriptomic profiles of interneuron classes are consistent throughout the cortex (Saunders et al., 2018; Tasic et al., 2018; Yao et al., 2021).”
BackgroundInfluentialSomatic CNV Detection by Single-Cell Whole-Genome Sequencing in Postmortem Human Brain
Pérez‐Rodríguez, D., Kalyva, M., Santucci, C., & Proukakis, C. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2655-9_11 · Cited by 5
MASI enables fast model-free standardization and integration of single-cell transcriptomics data
Xu, Y., Kramann, R., McCord, R. P., & Hayat, S. (2023) · Communications Biology · doi:10.1038/s42003-023-04820-3 · Cited by 4
Multiple Intellegences-based learning innovation towards Era 5.0
Maja, G., María-José, C., Anindya, S., Malin, A., & Karin, L. (2023) · World Psychology · doi:10.55849/wp.v1i3.382 · Cited by 5
“(Adl et al., 2019; Pasolli et al., 2019; Saunders et al., 2018).”
Integrating physiological and transcriptomic analyses at the single-neuron level
Yagishita, H., & Sasaki, T. (2024) · Neuroscience Research · doi:10.1016/j.neures.2024.05.003 · Cited by 4
“…addition, numerous studies from molecular biology have elucidated the gene expression and molecular characteristics of diverse neuronal types in the brain (Graves et al. , 2012; Thome et al. , 2014; Cembrowski et al. , 2016; Habib et al. , 2016; Saunders et al. , 2018; Cembrowski & Spruston, 2019).”
BackgroundKim, H., Park, G., Shin, H. G., et al. (2024) · Journal of Neuroscience · doi:10.1523/jneurosci.1473-24.2024 · Cited by 4
Cholecystokinin Modulates Corticostriatal Transmission and Plasticity in Rodents
Guillaume, C., Sáez, M., Parnet, P., Reig, R., & Paillé, V. (2025) · eNeuro · doi:10.1523/eneuro.0251-24.2025 · Cited by 4
A Mutation in the ANK2 Gene Causing ASD and a Review of the Literature
Zhao, L., Qiao, Z., Jia, Y., Fu, J., Li, T., Jia, K., Zhao, H. C., Bao, J., Yang, X., Pan, H., et al. (2025) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.70083 · Cited by 4
“AnkB is widely expressed in both excitatory and inhibitory neurons of the brain, as well as in glial cells (Saunders et al. 2018; Jin et al. 2020).”
Jahncke, J. N., Schnell, E., & Wright, K. M. (2025) · Communications Biology · doi:10.1038/s42003-025-08323-1 · Cited by 4
“All three Neurexins are expressed by MLIs, and these neurons have been dif fi cult to selectively target for conditional deletions 57,58 .”
Brill-Weil, S. G., Kramer, P. F., Yanez, A., Lipkin, A. M., Clever, F. H., Zhang, R., & Khaliq, Z. M. (2025) · Cell Reports · doi:10.1016/j.celrep.2025.116555 · Cited by 5
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Generation of an Lpar1-EGFP Fusion Knock-in Transgenic Mouse Line
Rivera, R. R., Williams, N., Kennedy, G. G., Sánchez-Pavón, P., & Chun, J. J. M. (2021) · Cell Biochemistry and Biophysics · doi:10.1007/s12013-021-01033-5 · Cited by 4
Parrish, R. R., Scott, C. A. M.-G., Walsh, D. A., Racca, C., Cowell, R. M., & Trevelyan, A. J. (2021) · Journal of Neurophysiology · doi:10.1152/jn.00295.2021 · Cited by 4
Houlton, J., Zubkova, O. V., & Clarkson, A. N. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23094817 · Cited by 4
Moreno, C., Jiao, S., Yano, S. T., et al. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac205 · Cited by 4
Jacobi, A., & Tran, N. M. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3012-9_1 · Cited by 3
Genetic labeling reveals spatial and cellular expression pattern of neuregulin 1 in mouse brain
Ding, C., Ding, Y., Ji, H., Wang, Y.-Y., Zhang, X., & Yin, D. (2023) · Cell & Bioscience · doi:10.1186/s13578-023-01032-4 · Cited by 4
New insights into CNS development from multiomics approaches
Solanelles-Farré, L., & Telley, L. (2020) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2020.09.010 · Cited by 4
A systematic evaluation of interneuron morphology representations for cell type discriminationPreprint
Laturnus, S., Kobak, D., & Berens, P. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/591370 · Cited by 6
A single-nuclei RNA sequencing study of Mendelian and sporadic AD in the human brain
Del-Aguila, J. L., Li, Z., Dube, U., Mihindukulasuriya, K. A., Budde, J. P., Fernandez, M. V., Ibanez, L., Bradley, J., Wang, F., Bergmann, K., et al. (2019) · doi:10.1101/593756 · Cited by 12
Mahadevan, V., Mitra, A., Zhang, Y., Yuan, X. Q., Peltekian, A., Chittajallu, R., Esnault, C., Maric, D., Rhodes, C. T., Pelkey, K. A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.10.144295 · Cited by 4
Comparative analysis of single-cell RNA Sequencing Platforms and MethodsPreprint
Ashton, J. M., Rehrauer, H., Myers, J. R., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.20.212100 · Cited by 4
“DropSeq technology encapsulates single cells of different sizes in an oil-based droplet along with barcoded beads attached to unique oligomers in individual aqueous droplets for gene expression profiling of many hundreds to thousands of cells in parallel 4 .”
MethodExpansion-Assisted Iterative-FISH defines lateral hypothalamus spatio-molecular organizationPreprint
Wang, Y., Eddison, M., Fleishman, G. M., Weigert, M., Xu, S., Henry, F. E., Wang, T., Lemire, A. L., Schmidt, U., Yang, H., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.03.08.434304 · Cited by 6
Dumrongprechachan, V., Soto, G., MacDonald, M. L., & Kozorovitskiy, Y. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.08.439091 · Cited by 4
Osorno, T., Rudolph, S., Nguyen, T., Kozareva, V., Nadaf, N. M., Macosko, E. Z., Lee, W.-C. A., & Regehr, W. G. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.09.439172 · Cited by 4
“Third, the application of single cell molecular approaches to the study of the CCs had also been limited by granule cells outnumbering most other cell types, especially interneurons (Saunders et al., 2018).”
BackgroundSelf-citationChen, R., Blosser, T. R., Djekidel, M. N., Hao, J., Bhattacherjee, A., Chen, W., Tuesta, L. M., Zhuang, X., & Zhang, Y. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.17.452808 · Cited by 4
Wang, A. Z., Bowman-Kirigin, J. A., Desai, R. R., Patel, P. R., Patel, B., Khan, S. M., Bender, D. E., Marlin, M. C., Liu, J., Osbun, J. W., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.03.454066 · Cited by 4
STEEL enables high-resolution delineation of spatiotemporal transcriptomic dataPreprint
Chen, Y., Zhou, S., Li, M., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1240258/v1 · Cited by 4
A comprehensive view of cell-type-specific temporal dynamics in human and mouse brainsPreprint
Lu, Z., Zhang, M., Lee, J., Sziraki, A., Anderson, S., Ge, S., Nelson, P. T., Zhou, W. Y., & Cao, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.01.509820 · Cited by 4
Akhshi, A., Haggard, M., Márquez, M. M., Farjami, S., Chacron, M. J., & Khadra, A. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.03.521866 · Cited by 4
Jiang, A., Lehnert, K., Reid, S. J., Handley, R. R., Jacobsen, J. C., Rudiger, S. R., McLaughlan, C. J., Verma, P. J., Bawden, C. S., & Snell, R. G. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.19.529150 · Cited by 5
Regulation of the Blood–Brain Barrier in Health and DiseasePreprint
Rada, C. C., Yuki, K., Ding, J., & Kuo, C. J. (2023) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a041191 · Cited by 4
Truby, N. L., Kim, R. K., Silva, G. M., Qu, X., Picone, J. A., Alemu, R., Neve, R. L., Cui, X., Liu, J., & Hamilton, P. J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.04.03.535374 · Cited by 4
Molecular logic for cellular specializations that initiate the auditory parallel processing pathwaysPreprint
Jing, J., Hu, M., Ngodup, T., Ma, Q., Lau, S.-N. N., Ljungberg, M. C., McGinley, M. J., Trussell, L. O., & Jiang, X. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.05.15.539065 · Cited by 4
Disentangling associations between complex traits and cell types with seismicPreprint
Lai, Q., Dannenfelser, R., Roussarie, J.-P., & Yao, V. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.04.592534 · Cited by 4
“…that overlap between TM FACS and these 2 additional scRNA-seq datasets, one using a different technology, and the other using human cells, we find consistent cell type-trait associations (Supplementary Figures [20] analyzed using cell type labels at different resolutions from Saunders et al. [21].”
InfluentialKynurenic acid inflammatory signaling expands in primates and impairs prefrontal cortical cognitionPreprint
Yang, S., Datta, D., Krienen, F. M., Ling, E., Woo, E., May, A., Anderson, G., Galvin, V. C., González‐Burgos, G., Lewis, D. A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.06.13.598842 · Cited by 4
Self-citationDecoding the Epigenetic Landscape: Insights into 5mC and 5hmC Patterns in Mouse Cortical Cell TypesPreprint
Wei, X., Li, J., Cheng, Z., Wei, S., Yu, G., & Olsen, M. L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.06.602342 · Cited by 4
Gaertner, Z. A., Oram, C., Schneeweis, A. K., Schonfeld, E., Bolduc, C., Chen, C., Dombeck, D. A., Parisiadou, L., Poulin, J. F., & Awatramani, R. B. (2024) · eLife · doi:10.7554/elife.101035.1 · Cited by 5
Shiraki, H., Segi‐Nishida, E., & Suzuki, K. (2025) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2025.151729 · Cited by 4
Uncovering the role of Gpr45 in obesity regulation
Karolczak, E. O., Li, C., Alcantara, I. C., Cohen, I. M., Gao, C., Xiao, C., Goldschmidt, A. I., Pinkus, C. A., Li, J., Li, M. M., et al. (2025) · Molecular Metabolism · doi:10.1016/j.molmet.2025.102174 · Cited by 4
Characterization of NMDA receptor Allostery modulation
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“The distribution of other markers often used in VP ( 23 , 27 , 39 ) varied from highly restricted in distinct clusters to very broadly expressed.”
Core Circadian Protein BMAL1: Implication for Nervous System Functioning and Its Diseases
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“In some brain areas, such as the posterior cortex and striatum, increased Bmal1 expression has been observed in inhibitory neurons, but in the hippocampus, Bmal1 expression is increased in excitatory neurons and astrocytes [37].”
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Metadata
- Authors
- Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., Krienen, F. M., de Rivera, H., Bien, E., Baum, M., Bortolin, L., Wang, S., Goeva, A., Nemesh, J., Kamitaki, N., Brumbaugh, S., Kulp, D., & McCarroll, S. A.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Saunders, Arpiar
- Macosko, Evan Z.
- McCarroll, Steven A.
- ORCID
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 174
- Issue
- 4
- Pages
- 1015-1030.e16
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6447408
- OpenAlex
- W2885927965
- Cited by
- 1,531
- References
- 84
- Citations by year
- 2026: 137 · 2025: 188 · 2024: 210 · 2023: 207 · 2022: 204 · 2021: 239 · 2020: 186 · 2019: 131 · 2018: 25
- FWCI
- 45.42
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (submittedVersion, cc-by-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Helen Hay Whitney Foundation
- Broad Institute
- National Institutes of Health (T32GM007753, T32MH020017)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- adult mouse brain, single-cell transcriptomics, cell types, basal ganglia, striatum, globus pallidus externus, substantia nigra pars reticulata, thalamus, hippocampus, cerebellum, cortical regions, voltage-gated ion channels, cerebral vasculature, glutamatergic neurons, medium spiny neurons, neuron classification, brain organization
- MeSH terms
- Animals, Brain, Male, Mice, Inbred C57BL, Gene Expression Regulation, Developmental, Cell Lineage, Gene Expression Profiling, Mice, Gene Regulatory Networks, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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